1. Fuzzy inference system for evaluating traffic congestion on urban streets.
- Author
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Shkera, Ali and Patankar, Vaishali
- Subjects
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TRAFFIC congestion , *FUZZY logic , *FUZZY systems , *CITY traffic , *TRAVEL time (Traffic engineering) , *TRAFFIC flow - Abstract
Traffic congestion is one of the mobility problems characterised in terms of travel time, speed, and social, environmental, and economic costs. Various factors such as the traveller's perception of acceptability, variation in congestion measurement, the traveller's perception of acceptability, and the unreliability of the measurement. One of the crucial issues for policymakers in identifying and quantifying the congestion levels. The aim is to develop a fuzzy-based methodology to emulate human expertise in measuring congestion levels in arterial streets for heterogeneous traffic flow using two input variables: Speed and Density. The outcome was a single congestion index value between (0 and 1). The (FMM) Fuzzy Mamdani model was implemented using MATLAB R2020. The memberships were defined for inputs and outputs variables, and fuzzy rules were developed based on the available parameters. The model used real-time traffic data on the major road network of Latakia city, Syria. The results from the fuzzy system compared with the results from using one parameter, the speed performance index. The results show notable differences between the two approaches and conclude that using fuzzy logic for congestion measure by combining two or more variables reflects and represents the ambiguous nature of congestion better than one input or index. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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